{"id":"https://openalex.org/W4226210750","doi":"https://doi.org/10.1109/ccis53392.2021.9754670","title":"Windows Attention Based Pyramid Network for Food Segmentation","display_name":"Windows Attention Based Pyramid Network for Food Segmentation","publication_year":2021,"publication_date":"2021-11-07","ids":{"openalex":"https://openalex.org/W4226210750","doi":"https://doi.org/10.1109/ccis53392.2021.9754670"},"language":"en","primary_location":{"id":"doi:10.1109/ccis53392.2021.9754670","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccis53392.2021.9754670","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001614863","display_name":"Xiaoxiao Dong","orcid":"https://orcid.org/0000-0003-0992-0594"},"institutions":[{"id":"https://openalex.org/I179026463","display_name":"Beijing Technology and Business University","ror":"https://ror.org/013e0zm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I179026463"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaoxiao Dong","raw_affiliation_strings":["Beijing Technology and Business University,School of Computer Science and Engineering,Beijing,China,10048","Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing","National Engineering Laboratory for Agri-Product Quality Traceability"],"affiliations":[{"raw_affiliation_string":"Beijing Technology and Business University,School of Computer Science and Engineering,Beijing,China,10048","institution_ids":["https://openalex.org/I179026463"]},{"raw_affiliation_string":"Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing","institution_ids":[]},{"raw_affiliation_string":"National Engineering Laboratory for Agri-Product Quality Traceability","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100391934","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0002-3143-0623"},"institutions":[{"id":"https://openalex.org/I179026463","display_name":"Beijing Technology and Business University","ror":"https://ror.org/013e0zm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I179026463"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["Beijing Technology and Business University,School of Computer Science and Engineering,Beijing,China,10048","Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing","National Engineering Laboratory for Agri-Product Quality Traceability"],"affiliations":[{"raw_affiliation_string":"Beijing Technology and Business University,School of Computer Science and Engineering,Beijing,China,10048","institution_ids":["https://openalex.org/I179026463"]},{"raw_affiliation_string":"Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing","institution_ids":[]},{"raw_affiliation_string":"National Engineering Laboratory for Agri-Product Quality Traceability","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108049287","display_name":"Haisheng Li","orcid":"https://orcid.org/0000-0003-4861-0513"},"institutions":[{"id":"https://openalex.org/I179026463","display_name":"Beijing Technology and Business University","ror":"https://ror.org/013e0zm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I179026463"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haisheng Li","raw_affiliation_strings":["Beijing Technology and Business University,School of Computer Science and Engineering,Beijing,China,10048","National Engineering Laboratory for Agri-Product Quality Traceability","Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing"],"affiliations":[{"raw_affiliation_string":"Beijing Technology and Business University,School of Computer Science and Engineering,Beijing,China,10048","institution_ids":["https://openalex.org/I179026463"]},{"raw_affiliation_string":"National Engineering Laboratory for Agri-Product Quality Traceability","institution_ids":[]},{"raw_affiliation_string":"Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087987156","display_name":"Qiang Cai","orcid":"https://orcid.org/0000-0002-4689-5191"},"institutions":[{"id":"https://openalex.org/I179026463","display_name":"Beijing Technology and Business University","ror":"https://ror.org/013e0zm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I179026463"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Cai","raw_affiliation_strings":["Beijing Technology and Business University,School of Computer Science and Engineering,Beijing,China,10048","National Engineering Laboratory for Agri-Product Quality Traceability","Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing"],"affiliations":[{"raw_affiliation_string":"Beijing Technology and Business University,School of Computer Science and Engineering,Beijing,China,10048","institution_ids":["https://openalex.org/I179026463"]},{"raw_affiliation_string":"National Engineering Laboratory for Agri-Product Quality Traceability","institution_ids":[]},{"raw_affiliation_string":"Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5001614863"],"corresponding_institution_ids":["https://openalex.org/I179026463"],"apc_list":null,"apc_paid":null,"fwci":0.3342,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.54687292,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"213","last_page":"217"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10866","display_name":"Nutritional Studies and Diet","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11584","display_name":"Biochemical Analysis and Sensing Techniques","score":0.9358999729156494,"subfield":{"id":"https://openalex.org/subfields/2916","display_name":"Nutrition and Dietetics"},"field":{"id":"https://openalex.org/fields/29","display_name":"Nursing"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8208047151565552},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.7831113338470459},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7159758806228638},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.68941730260849},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5413148403167725},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5391831994056702},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5346454977989197},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4884876012802124},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4810529053211212},{"id":"https://openalex.org/keywords/segmentation-based-object-categorization","display_name":"Segmentation-based object categorization","score":0.4265854060649872},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.402563214302063},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38310113549232483}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8208047151565552},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.7831113338470459},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7159758806228638},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.68941730260849},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5413148403167725},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5391831994056702},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5346454977989197},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4884876012802124},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4810529053211212},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.4265854060649872},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.402563214302063},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38310113549232483},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccis53392.2021.9754670","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccis53392.2021.9754670","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 7th International Conference on Cloud Computing and Intelligent Systems (CCIS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger","score":0.6399999856948853}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321793","display_name":"Beijing Municipal Education Commission","ror":"https://ror.org/04bpn6s66"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1159302035","https://openalex.org/W1496513042","https://openalex.org/W1903029394","https://openalex.org/W1997709480","https://openalex.org/W2037561382","https://openalex.org/W2133059825","https://openalex.org/W2152632881","https://openalex.org/W2194775991","https://openalex.org/W2206370378","https://openalex.org/W2412782625","https://openalex.org/W2560023338","https://openalex.org/W2727229559","https://openalex.org/W2777712566","https://openalex.org/W2900903825","https://openalex.org/W2910628332","https://openalex.org/W2954801189","https://openalex.org/W2955058313","https://openalex.org/W2955957825","https://openalex.org/W2963881378","https://openalex.org/W2972610293","https://openalex.org/W2980441700","https://openalex.org/W2981771452","https://openalex.org/W3009098945","https://openalex.org/W3021194804","https://openalex.org/W3038013995","https://openalex.org/W3094502228","https://openalex.org/W3108413361","https://openalex.org/W3138516171","https://openalex.org/W3170706004","https://openalex.org/W3170841864","https://openalex.org/W3207251446","https://openalex.org/W6687483927","https://openalex.org/W6782894772","https://openalex.org/W6784333009"],"related_works":["https://openalex.org/W3144569342","https://openalex.org/W2945274617","https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W2055202857","https://openalex.org/W2371519352","https://openalex.org/W4205800335","https://openalex.org/W2386644571","https://openalex.org/W2372421320","https://openalex.org/W2551987074"],"abstract_inverted_index":{"Recently,":[0],"food":[1,35,43,59,78,156,178],"segmentation":[2,52,80,151],"has":[3],"obtained":[4],"growing":[5],"attention":[6],"in":[7,17],"the":[8,77],"field":[9],"of":[10,21,42],"computer":[11],"vision":[12],"for":[13,34,181],"its":[14],"great":[15],"potential":[16],"human":[18],"health.":[19],"Most":[20],"existing":[22],"methods":[23],"utilize":[24,139],"deep":[25],"visual":[26],"features":[27,112],"extracting":[28],"from":[29],"Convolutional":[30],"Neural":[31],"Networks":[32],"(CNNs)":[33],"segmentation.":[36,183],"However,":[37],"these":[38],"works":[39,91],"ignore":[40],"characteristics":[41],"images":[44,60],"and":[45,68,86,130,162],"are":[46],"thus":[47],"difficult":[48],"to":[49,108,126,168],"achieve":[50],"optimal":[51],"performance.":[53],"Compared":[54],"with":[55,113,123,153],"general":[56],"image":[57,79,93,179],"segmentation,":[58],"usually":[61],"do":[62],"not":[63],"exhibit":[64],"unique":[65],"spatial":[66],"layout":[67],"common":[69],"semantic":[70,155,182],"patterns.":[71],"In":[72,136],"this":[73],"paper,":[74],"we":[75,99,138],"address":[76],"task":[81],"by":[82,95,159],"capturing":[83],"richer":[84,133],"contextual":[85],"boundary":[87],"information.":[88,135],"The":[89],"previous":[90],"capture":[92],"representation":[94],"multi-scale":[96,128],"feature":[97],"fusion,":[98],"propose":[100],"a":[101,140,175],"Windows":[102,163],"Attention":[103,125],"based":[104],"Pyramid":[105,120],"Network":[106,121],"(WAPNet)":[107],"adaptively":[109],"combine":[110],"local":[111],"global":[114],"dependencies.":[115],"Specifically,":[116],"WAPNet":[117],"combines":[118],"Feature":[119],"(FPN)":[122],"Window":[124],"weight":[127],"features,":[129],"then":[131],"extract":[132],"marginal":[134],"addition,":[137],"multimodality":[141],"pre-training":[142],"approach":[143],"Recipe":[144],"Learning":[145],"Module":[146],"(ReLeM)":[147],"that":[148],"explicitly":[149],"provides":[150],"model":[152],"rich":[154],"knowledge.":[157],"And":[158],"introducing":[160],"Locality":[161],"design,":[164],"calculating":[165],"self-attention":[166],"according":[167],"Windows,":[169],"We":[170],"demonstrate":[171],"promising":[172],"performance":[173],"on":[174],"new":[176],"proposed":[177],"benchmark":[180]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}